The effect of TLS point cloud sampling on tree detection and diameter measurement accuracy

Terrestrial laser scanning (TLS) is a vital technique for collecting data with millimetre-level detail from the surrounding forest or individual trees. The downside of the technique is the vast amount of data it can collect, which requires efficient data processing methods. An increasing number of manual and automatic methods have been developed in recent years and most of them use point cloud sampling to fasten the processing. The challenge in the sampling procedures is the point density of TLS, which decreases rapidly as a function of distance from the scanner location. Therefore, traditional sampling procedures are not suitable for processing TLS point clouds. The present study focuses on evaluating two sampling procedures (presented in Puttonen et al. 2013) that aim to reduce the point density without losing the characteristics of the full point cloud. The study goal was to assess the effect of these two sampling procedures in tree detection and diameter at breast height (1.3 m, DBH) measurement accuracies in two sample cases. The results demonstrated that the point cloud sampling could be effectively used without losing accuracy in tree detection. However, for measurement of single-tree attributes, the use of full point cloud is recommended. Further evaluation of the methods is required with more diverse data set and due to the manual processing applied here. The automatic approach is mandatory if the approach is to be considered for more operational use.